Dataset Information

Acceleration Data Reveal Highly Individually Structured Energetic Landscapes in Free-Ranging Fishers (Pekania pennanti).

ABSTRACT: Investigating animal energy expenditure across space and time may provide more detailed insight into how animals interact with their environment. This insight should improve our understanding of how changes in the environment affect animal energy budgets and is particularly relevant for animals living near or within human altered environments where habitat change can occur rapidly. We modeled fisher (Pekania pennanti) energy expenditure within their home ranges and investigated the potential environmental and spatial drivers of the predicted spatial patterns. As a proxy for energy expenditure we used overall dynamic body acceleration (ODBA) that we quantified from tri-axial accelerometer data during the active phases of 12 individuals. We used a generalized additive model (GAM) to investigate the spatial distribution of ODBA by associating the acceleration data to the animals' GPS-recorded locations. We related the spatial patterns of ODBA to the utilization distributions and habitat suitability estimates across individuals. The ODBA of fishers appears highly structured in space and was related to individual utilization distribution and habitat suitability estimates. However, we were not able to predict ODBA using the environmental data we selected. Our results suggest an unexpected complexity in the space use of animals that was only captured partially by re-location data-based concepts of home range and habitat suitability. We suggest future studies recognize the limits of ODBA that arise from the fact that acceleration is often collected at much finer spatio-temporal scales than the environmental data and that ODBA lacks a behavioral correspondence. Overcoming these limits would improve the interpretation of energy expenditure in relation to the environment.

SUBMITTER: Scharf AK

PROVIDER: S-EPMC4739643 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Acceleration Data Reveal Highly Individually Structured Energetic Landscapes in Free-Ranging Fishers (Pekania pennanti).

Scharf Anne K AK LaPoint Scott S Wikelski Martin M Safi Kamran K

PloS one 20160203 2

Investigating animal energy expenditure across space and time may provide more detailed insight into how animals interact with their environment. This insight should improve our understanding of how changes in the environment affect animal energy budgets and is particularly relevant for animals living near or within human altered environments where habitat change can occur rapidly. We modeled fisher (Pekania pennanti) energy expenditure within their home ranges and investigated the potential env ...[more]

PMID: 26840399

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Project description:The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal-borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under-acknowledged consideration in biologging is the trade-off between sampling rate and sampling duration, created by battery- (or memory-) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low-frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free-ranging red squirrels (200-300 g) that were fitted with accelerometers (2 g) recording tri-axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long-duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi-month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior.

Dataset Information

Acceleration Data Reveal Highly Individually Structured Energetic Landscapes in Free-Ranging Fishers (Pekania pennanti).

Publications

Acceleration Data Reveal Highly Individually Structured Energetic Landscapes in Free-Ranging Fishers (Pekania pennanti).

Similar Datasets

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